Lung Cancer Classification and Gene Selection by Combining Affinity Propagation Clustering and Sparse Group Lasso

(E-pub Ahead of Print)

Author(s): Juntao Li*, Mingming Chang, Qinghui Gao, Xuekun Song, Zhiyu Gao

Journal Name: Current Bioinformatics

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Background: Cancer threatens human health seriously. Diagnosing cancer via gene expression analysis is the hot topic in cancer research.

Objective: To diagnose the accurate type of lung cancer and discover the pathogenic genes.

Method: In this study, affinity propagation (AP) clustering with similarity score is employed to each type of lung cancer and normal lung. After grouping genes, sparse group lasso is adopted to construct four binary classifiers and the voting strategy is used to integrate them.

Results: This study screens six gene groups that may associate with diffierent lung cancer subtypes among 73 genes groups, and identifies three possible key pathogenic genes, KRAS, BRAF and VDR. Furthermore, this study achieves improved classification accuracies at minority classes SQ and COID in comparison with other four methods.

Conclusion: We propose the AP clustering based sparse group lasso (AP-SGL), which provides an alternative for simultaneous diagnosis and gene selection for lung cancer.

Keywords: Lung cancer, Gene selection, Affinity propagation clustering, Sparse group lasso, Multi-classification

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Article Details

(E-pub Ahead of Print)
DOI: 10.2174/1574893614666191017103557
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